# Large Language Models for Healthcare Data Augmentation: An Example on Patient-Trial Matching

arXiv: https://arxiv.org/abs/2303.16756 · HTML: https://mlsanity.com/paper/2303.16756/

## Key facts

- Cited by 30 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 36 references that are in the map.
- First posted on arXiv 24 Mar 2023.

## Builds on (most cited first, 17 of 17)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [Publicly Available Clinical BERT Embeddings](https://mlsanity.com/paper/1904.03323/) (2019)
- [EDA: Easy Data Augmentation Techniques for Boosting Performance on Text Classification Tasks](https://mlsanity.com/paper/1901.11196/) (2019)
- [Improving Neural Machine Translation Models with Monolingual Data](https://mlsanity.com/paper/1511.06709/) (2015)
- [Memory Networks](https://mlsanity.com/paper/1410.3916/) (2014)
- [Synthetic and Natural Noise Both Break Neural Machine Translation](https://mlsanity.com/paper/1711.02173/) (2017)
- [Highway Networks](https://mlsanity.com/paper/1505.00387/) (2015)
- [Contextual Augmentation: Data Augmentation by Words with Paradigmatic Relations](https://mlsanity.com/paper/1805.06201/) (2018)
- [Data Augmentation using Pre-trained Transformer Models](https://mlsanity.com/paper/2003.02245/) (2020)
- [Does Synthetic Data Generation of LLMs Help Clinical Text Mining?](https://mlsanity.com/paper/2303.04360/) (2023)
- [Counter-fitting Word Vectors to Linguistic Constraints](https://mlsanity.com/paper/1603.00892/) (2016)
- [COMPOSE: Cross-Modal Pseudo-Siamese Network for Patient Trial Matching](https://mlsanity.com/paper/2006.08765/) (2020)
- [Adversarial Over-Sensitivity and Over-Stability Strategies for Dialogue Models](https://mlsanity.com/paper/1809.02079/) (2018)
- [DivAug: Plug-in Automated Data Augmentation with Explicit Diversity Maximization](https://mlsanity.com/paper/2103.14545/) (2021)
- [Learning Eligibility in Cancer Clinical Trials using Deep Neural Networks](https://mlsanity.com/paper/1803.08312/) (2018)

## Built on by (most cited first, 30 of 30)

- [KIVI: A Tuning-Free Asymmetric 2bit Quantization for KV Cache](https://mlsanity.com/paper/2402.02750/) (2024)
- [Matching Patients to Clinical Trials with Large Language Models](https://mlsanity.com/paper/2307.15051/) (2023)
- [KV Cache Compression, But What Must We Give in Return? A Comprehensive Benchmark of Long Context Capable Approaches](https://mlsanity.com/paper/2407.01527/) (2024)
- [Data Augmentation using Large Language Models: Data Perspectives, Learning Paradigms and Challenges](https://mlsanity.com/paper/2403.02990/) (2024)
- [Data-Centric Foundation Models in Computational Healthcare: A Survey](https://mlsanity.com/paper/2401.02458/) (2024)
- [Assessing and Enhancing Large Language Models in Rare Disease Question-answering](https://mlsanity.com/paper/2408.08422/) (2024)
- [DHP Benchmark: Are LLMs Good NLG Evaluators?](https://mlsanity.com/paper/2408.13704/) (2024)
- [Panacea: A foundation model for clinical trial search, summarization, design, and recruitment](https://mlsanity.com/paper/2407.11007/) (2024)
- [Digital Diagnostics: The Potential Of Large Language Models In Recognizing Symptoms Of Common Illnesses](https://mlsanity.com/paper/2405.06712/) (2024)
- [Unlocking LLMs: Addressing Scarce Data and Bias Challenges in Mental Health](https://mlsanity.com/paper/2412.12981/) (2024)
- [Measuring Diversity in Synthetic Datasets](https://mlsanity.com/paper/2502.08512/) (2025)
- [Cocktail: Chunk-Adaptive Mixed-Precision Quantization for Long-Context LLM Inference](https://mlsanity.com/paper/2503.23294/) (2025)
- [Aligning Actions and Walking to LLM-Generated Textual Descriptions](https://mlsanity.com/paper/2404.12192/) (2024)
- [Large Language Models for Healthcare Text Classification: A Systematic Review](https://mlsanity.com/paper/2503.01159/) (2025)
- [ChemActor: Enhancing Automated Extraction of Chemical Synthesis Actions with LLM-Generated Data](https://mlsanity.com/paper/2506.23520/) (2025)
- [A ML-LLM pairing for better code comment classification](https://mlsanity.com/paper/2310.10275/) (2023)
- [Data Augmentation of Multi-turn Psychological Dialogue via Knowledge-driven Progressive Thought Prompting](https://mlsanity.com/paper/2406.16567/) (2024)
- [Rephrasing Electronic Health Records for Pretraining Clinical Language Models](https://mlsanity.com/paper/2411.18940/) (2024)
- [How Private are Language Models in Abstractive Summarization?](https://mlsanity.com/paper/2412.12040/) (2024)
- [Clinical trial cohort selection using Large Language Models on n2c2 Challenges](https://mlsanity.com/paper/2501.11114/) (2025)
- [Survey and Improvement Strategies for Gene Prioritization with Large Language Models](https://mlsanity.com/paper/2501.18794/) (2025)
- [Maximum Redundancy Pruning: A Principle-Driven Layerwise Sparsity Allocation for LLMs](https://mlsanity.com/paper/2503.18377/) (2025)
- [Natural Language Processing in Support of Evidence-based Medicine: A Scoping Review](https://mlsanity.com/paper/2505.22280/) (2025)
- [A Survey on LLM-Assisted Clinical Trial Recruitment](https://mlsanity.com/paper/2506.15301/) (2025)
- [Integrating curation into scientific publishing to train AI models](https://mlsanity.com/paper/2310.20440/) (2023)
- [On The Role of Prompt Construction In Enhancing Efficacy and Efficiency of LLM-Based Tabular Data Generation](https://mlsanity.com/paper/2409.03946/) (2024)
- [Evaluating the effectiveness of LLM-based interoperability](https://mlsanity.com/paper/2510.23893/) (2025)
- [CLaDMoP: Learning Transferrable Models from Successful Clinical Trials via LLMs](https://mlsanity.com/paper/2505.18527/) (2025)
- [A systematic review of trial-matching pipelines using large language models](https://mlsanity.com/paper/2509.19327/) (2025)
- [Mitigating Semantic Drift: Evaluating LLMs' Efficacy in Psychotherapy through MI Dialogue Summarization](https://mlsanity.com/paper/2511.22818/) (2025)

## Abstract (arXiv)

The process of matching patients with suitable clinical trials is essential for advancing medical research and providing optimal care. However, current approaches face challenges such as data standardization, ethical considerations, and a lack of interoperability between Electronic Health Records (EHRs) and clinical trial criteria. In this paper, we explore the potential of large language models (LLMs) to address these challenges by leveraging their advanced natural language generation capabilities to improve compatibility between EHRs and clinical trial descriptions. We propose an innovative privacy-aware data augmentation approach for LLM-based patient-trial matching (LLM-PTM), which balances the benefits of LLMs while ensuring the security and confidentiality of sensitive patient data. Our experiments demonstrate a 7.32% average improvement in performance using the proposed LLM-PTM method, and the generalizability to new data is improved by 12.12%. Additionally, we present case studies to further illustrate the effectiveness of our approach and provide a deeper understanding of its underlying principles.